gpCAM
Required: gpcam
Example usage: test_gpCAM
This module provides persistent generators for Gaussian Process-based adaptive sampling.
Persistent generator exposing gpCAM functionality
- persistent_gpCAM.persistent_gpCAM(H_in, persis_info, gen_specs, libE_info)
This generation function constructs a global surrogate of f values. It is a batched method that produces a first batch uniformly random from (lb, ub). On subequent iterations, it calls an optimization method to produce the next batch of points. This optimization might be too slow (relative to the simulation evaluation time) for some use cases.
See also
- persistent_gpCAM.persistent_gpCAM_covar(H_in, persis_info, gen_specs, libE_info)
This generation function constructs a global surrogate of f values. It is a batched method that produces a first batch uniformly random from (lb, ub) and on following iterations samples the GP posterior covariance function to find sample points.
If gen_specs[“user”][“use_grid”] is set to True, the parameter space is divided into a mesh of candidate points (num_points in each dimension). Subsequent points are chosen with maximum covariance that are at least a distance r away from each other to explore difference regions.
If gen_specs[“user”][“test_points_file”] is set to a file of evaluated points, then the gpCAM predications are compared at these points to assess model quality.
See also